Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations4000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory468.9 KiB
Average record size in memory120.0 B

Variable types

Numeric11
Categorical4

Alerts

acidity is highly overall correlated with contaminant_level and 3 other fieldsHigh correlation
anomaly is highly overall correlated with load and 1 other fieldsHigh correlation
contaminant_level is highly overall correlated with acidity and 3 other fieldsHigh correlation
failure is highly overall correlated with acidity and 2 other fieldsHigh correlation
load is highly overall correlated with anomalyHigh correlation
oil_quality is highly overall correlated with acidity and 3 other fieldsHigh correlation
pressure is highly overall correlated with process_type_VibrationsHigh correlation
process_type_Oil Analysis is highly overall correlated with acidity and 2 other fieldsHigh correlation
process_type_Vibrations is highly overall correlated with pressure and 2 other fieldsHigh correlation
temperature is highly overall correlated with process_type_Vibrations and 1 other fieldsHigh correlation
vibration is highly overall correlated with anomaly and 2 other fieldsHigh correlation
anomaly is highly imbalanced (86.1%) Imbalance
maintenance_history has 185 (4.6%) zeros Zeros

Reproduction

Analysis started2025-08-12 21:14:35.099281
Analysis finished2025-08-12 21:14:38.899524
Duration3.8 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

equipment_id
Real number (ℝ)

Distinct100
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:38.924638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation28.869679
Coefficient of variation (CV)0.57167681
Kurtosis-1.2002402
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum202000
Variance833.45836
MonotonicityIncreasing
2025-08-12T23:14:38.955774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 40
 
1.0%
64 40
 
1.0%
74 40
 
1.0%
73 40
 
1.0%
72 40
 
1.0%
71 40
 
1.0%
70 40
 
1.0%
69 40
 
1.0%
68 40
 
1.0%
67 40
 
1.0%
Other values (90) 3600
90.0%
ValueCountFrequency (%)
1 40
1.0%
2 40
1.0%
3 40
1.0%
4 40
1.0%
5 40
1.0%
6 40
1.0%
7 40
1.0%
8 40
1.0%
9 40
1.0%
10 40
1.0%
ValueCountFrequency (%)
100 40
1.0%
99 40
1.0%
98 40
1.0%
97 40
1.0%
96 40
1.0%
95 40
1.0%
94 40
1.0%
93 40
1.0%
92 40
1.0%
91 40
1.0%

time_step
Real number (ℝ)

Distinct40
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.5
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:38.983326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.95
Q110.75
median20.5
Q330.25
95-th percentile38.05
Maximum40
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.54484
Coefficient of variation (CV)0.56316291
Kurtosis-1.2015027
Mean20.5
Median Absolute Deviation (MAD)10
Skewness0
Sum82000
Variance133.28332
MonotonicityNot monotonic
2025-08-12T23:14:39.011251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 100
 
2.5%
2 100
 
2.5%
23 100
 
2.5%
24 100
 
2.5%
25 100
 
2.5%
26 100
 
2.5%
27 100
 
2.5%
28 100
 
2.5%
29 100
 
2.5%
30 100
 
2.5%
Other values (30) 3000
75.0%
ValueCountFrequency (%)
1 100
2.5%
2 100
2.5%
3 100
2.5%
4 100
2.5%
5 100
2.5%
6 100
2.5%
7 100
2.5%
8 100
2.5%
9 100
2.5%
10 100
2.5%
ValueCountFrequency (%)
40 100
2.5%
39 100
2.5%
38 100
2.5%
37 100
2.5%
36 100
2.5%
35 100
2.5%
34 100
2.5%
33 100
2.5%
32 100
2.5%
31 100
2.5%

vibration
Real number (ℝ)

High correlation 

Distinct1241
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33852518
Minimum-4.5527291
Maximum19.26594
Zeros0
Zeros (%)0.0%
Negative514
Negative (%)12.8%
Memory size31.4 KiB
2025-08-12T23:14:39.041017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.5527291
5-th percentile-0.75401787
Q10.33852518
median0.33852518
Q30.33852518
95-th percentile1.099807
Maximum19.26594
Range23.818669
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.86746994
Coefficient of variation (CV)2.5624975
Kurtosis134.876
Mean0.33852518
Median Absolute Deviation (MAD)0
Skewness8.7957465
Sum1354.1007
Variance0.7525041
MonotonicityNot monotonic
2025-08-12T23:14:39.070497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3385251824 2760
69.0%
-0.01978559879 1
 
< 0.1%
0.7646775597 1
 
< 0.1%
0.1793443861 1
 
< 0.1%
0.4181354551 1
 
< 0.1%
0.3374469317 1
 
< 0.1%
-0.4631351623 1
 
< 0.1%
-0.2773219268 1
 
< 0.1%
-0.1169483846 1
 
< 0.1%
-0.9338329121 1
 
< 0.1%
Other values (1231) 1231
30.8%
ValueCountFrequency (%)
-4.552729071 1
< 0.1%
-2.527283068 1
< 0.1%
-2.222775719 1
< 0.1%
-2.204859043 1
< 0.1%
-1.947263098 1
< 0.1%
-1.896693476 1
< 0.1%
-1.808923044 1
< 0.1%
-1.791784707 1
< 0.1%
-1.774855476 1
< 0.1%
-1.737076395 1
< 0.1%
ValueCountFrequency (%)
19.26593972 1
< 0.1%
14.83407352 1
< 0.1%
14.02910446 1
< 0.1%
11.96450683 1
< 0.1%
11.23638754 1
< 0.1%
10.44843489 1
< 0.1%
9.92231788 1
< 0.1%
9.821453582 1
< 0.1%
9.754342506 1
< 0.1%
9.609780797 1
< 0.1%

temperature
Real number (ℝ)

High correlation 

Distinct1241
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.319115
Minimum15.064066
Maximum25.214408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:39.099425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.064066
5-th percentile18.4102
Q120.319115
median20.319115
Q320.319115
95-th percentile22.080838
Maximum25.214408
Range10.150342
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.99088321
Coefficient of variation (CV)0.04876606
Kurtosis5.0799999
Mean20.319115
Median Absolute Deviation (MAD)0
Skewness-0.15816733
Sum81276.461
Variance0.98184953
MonotonicityNot monotonic
2025-08-12T23:14:39.128583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.31911532 2760
69.0%
19.62624972 1
 
< 0.1%
20.44453108 1
 
< 0.1%
20.49638689 1
 
< 0.1%
21.21262526 1
 
< 0.1%
20.35977783 1
 
< 0.1%
18.75674636 1
 
< 0.1%
18.83398101 1
 
< 0.1%
19.14866142 1
 
< 0.1%
18.36840438 1
 
< 0.1%
Other values (1231) 1231
30.8%
ValueCountFrequency (%)
15.06406614 1
< 0.1%
15.66872209 1
< 0.1%
15.78702392 1
< 0.1%
15.92900322 1
< 0.1%
15.93527615 1
< 0.1%
16.00251616 1
< 0.1%
16.13039471 1
< 0.1%
16.15806844 1
< 0.1%
16.23573252 1
< 0.1%
16.30278027 1
< 0.1%
ValueCountFrequency (%)
25.21440809 1
< 0.1%
24.67752949 1
< 0.1%
24.58316772 1
< 0.1%
24.57360435 1
< 0.1%
24.56290989 1
< 0.1%
24.56080795 1
< 0.1%
24.50170167 1
< 0.1%
24.36682152 1
< 0.1%
24.22198103 1
< 0.1%
24.21570648 1
< 0.1%

pressure
Real number (ℝ)

High correlation 

Distinct1241
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.282761
Minimum26.400842
Maximum52.184114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:39.157636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.400842
5-th percentile29.967986
Q132.282761
median32.282761
Q332.282761
95-th percentile34.872444
Maximum52.184114
Range25.783272
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5941638
Coefficient of variation (CV)0.049381271
Kurtosis25.220954
Mean32.282761
Median Absolute Deviation (MAD)0
Skewness3.2271854
Sum129131.04
Variance2.5413582
MonotonicityNot monotonic
2025-08-12T23:14:39.186001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.28276098 2760
69.0%
28.60664597 1
 
< 0.1%
32.46810828 1
 
< 0.1%
31.01020488 1
 
< 0.1%
31.30778199 1
 
< 0.1%
28.77113625 1
 
< 0.1%
31.21584948 1
 
< 0.1%
29.31509006 1
 
< 0.1%
28.5894412 1
 
< 0.1%
33.52558134 1
 
< 0.1%
Other values (1231) 1231
30.8%
ValueCountFrequency (%)
26.40084195 1
< 0.1%
26.55559333 1
< 0.1%
27.52825657 1
< 0.1%
27.59802013 1
< 0.1%
27.69647323 1
< 0.1%
27.8036281 1
< 0.1%
27.80557363 1
< 0.1%
27.8958668 1
< 0.1%
27.95272842 1
< 0.1%
27.96291467 1
< 0.1%
ValueCountFrequency (%)
52.18411361 1
< 0.1%
51.65011508 1
< 0.1%
49.14100592 1
< 0.1%
44.70853885 1
< 0.1%
44.59190327 1
< 0.1%
44.3989945 1
< 0.1%
43.74444318 1
< 0.1%
43.51918091 1
< 0.1%
42.70516218 1
< 0.1%
42.48020498 1
< 0.1%

oil_quality
Real number (ℝ)

High correlation 

Distinct1401
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.894491
Minimum0.54954536
Maximum175.61584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:39.213823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.54954536
5-th percentile15.988542
Q153.894491
median53.894491
Q353.894491
95-th percentile89.953255
Maximum175.61584
Range175.0663
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.600534
Coefficient of variation (CV)0.34512866
Kurtosis4.8074864
Mean53.894491
Median Absolute Deviation (MAD)0
Skewness0.47160876
Sum215577.96
Variance345.97985
MonotonicityNot monotonic
2025-08-12T23:14:39.244028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.89449085 2600
65.0%
20.35188718 1
 
< 0.1%
14.3817669 1
 
< 0.1%
29.99583271 1
 
< 0.1%
60.81847871 1
 
< 0.1%
64.25033073 1
 
< 0.1%
58.52394424 1
 
< 0.1%
52.84464281 1
 
< 0.1%
66.3178126 1
 
< 0.1%
9.307491615 1
 
< 0.1%
Other values (1391) 1391
34.8%
ValueCountFrequency (%)
0.5495453601 1
< 0.1%
0.7252982613 1
< 0.1%
0.808874824 1
< 0.1%
1.395362571 1
< 0.1%
1.845149606 1
< 0.1%
1.907855259 1
< 0.1%
2.200192351 1
< 0.1%
2.208063918 1
< 0.1%
2.218839956 1
< 0.1%
2.30858453 1
< 0.1%
ValueCountFrequency (%)
175.6158417 1
< 0.1%
171.0250345 1
< 0.1%
170.253696 1
< 0.1%
166.5030622 1
< 0.1%
165.8295442 1
< 0.1%
165.5061761 1
< 0.1%
162.0981208 1
< 0.1%
160.3314709 1
< 0.1%
160.0998106 1
< 0.1%
157.061226 1
< 0.1%

contaminant_level
Real number (ℝ)

High correlation 

Distinct1401
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.129293
Minimum37.977304
Maximum110.57924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:39.272620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum37.977304
5-th percentile57.653904
Q176.129293
median76.129293
Q376.129293
95-th percentile94.282876
Maximum110.57924
Range72.601938
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.0842796
Coefficient of variation (CV)0.11932699
Kurtosis2.6366401
Mean76.129293
Median Absolute Deviation (MAD)0
Skewness-0.083859264
Sum304517.17
Variance82.524135
MonotonicityNot monotonic
2025-08-12T23:14:39.301966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.12929339 2600
65.0%
52.57710418 1
 
< 0.1%
56.29651043 1
 
< 0.1%
67.62050353 1
 
< 0.1%
85.33291278 1
 
< 0.1%
82.41494121 1
 
< 0.1%
84.30932048 1
 
< 0.1%
74.77057517 1
 
< 0.1%
82.91786437 1
 
< 0.1%
54.557562 1
 
< 0.1%
Other values (1391) 1391
34.8%
ValueCountFrequency (%)
37.97730369 1
< 0.1%
41.01876462 1
< 0.1%
42.61145887 1
< 0.1%
42.75120032 1
< 0.1%
43.10642784 1
< 0.1%
44.06433186 1
< 0.1%
44.07885378 1
< 0.1%
45.271787 1
< 0.1%
45.77655761 1
< 0.1%
46.24692081 1
< 0.1%
ValueCountFrequency (%)
110.5792412 1
< 0.1%
109.9514285 1
< 0.1%
108.9905806 1
< 0.1%
108.9722518 1
< 0.1%
107.2917118 1
< 0.1%
107.2350522 1
< 0.1%
106.0771311 1
< 0.1%
105.5808666 1
< 0.1%
105.3259949 1
< 0.1%
105.1955847 1
< 0.1%

acidity
Real number (ℝ)

High correlation 

Distinct1401
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.17143
Minimum7.50982
Maximum322.77222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:39.330481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.50982
5-th percentile29.525258
Q1138.17143
median138.17143
Q3138.17143
95-th percentile261.21372
Maximum322.77222
Range315.2624
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.302792
Coefficient of variation (CV)0.40024765
Kurtosis2.1643457
Mean138.17143
Median Absolute Deviation (MAD)0
Skewness0.5169727
Sum552685.74
Variance3058.3988
MonotonicityNot monotonic
2025-08-12T23:14:39.360203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.1714348 2600
65.0%
36.93617273 1
 
< 0.1%
27.47909091 1
 
< 0.1%
58.73672658 1
 
< 0.1%
153.1938621 1
 
< 0.1%
160.8731765 1
 
< 0.1%
147.950427 1
 
< 0.1%
125.3405615 1
 
< 0.1%
172.0550582 1
 
< 0.1%
20.25228158 1
 
< 0.1%
Other values (1391) 1391
34.8%
ValueCountFrequency (%)
7.509819989 1
< 0.1%
8.183140934 1
< 0.1%
8.286430962 1
< 0.1%
8.472405836 1
< 0.1%
8.714204083 1
< 0.1%
8.78641076 1
< 0.1%
8.898123466 1
< 0.1%
9.066569262 1
< 0.1%
10.35332385 1
< 0.1%
10.89048904 1
< 0.1%
ValueCountFrequency (%)
322.7722175 1
< 0.1%
322.5393007 1
< 0.1%
322.4896965 1
< 0.1%
321.0620056 1
< 0.1%
320.4563097 1
< 0.1%
320.1208436 1
< 0.1%
319.8804776 1
< 0.1%
319.6787152 1
< 0.1%
319.2675356 1
< 0.1%
319.0765003 1
< 0.1%

hours_operated
Real number (ℝ)

Distinct1361
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.368525
Minimum1.5389535
Maximum426.87586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:39.389536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.5389535
5-th percentile17.532353
Q161.368525
median61.368525
Q361.368525
95-th percentile111.96839
Maximum426.87586
Range425.33691
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.920583
Coefficient of variation (CV)0.50385084
Kurtosis20.002199
Mean61.368525
Median Absolute Deviation (MAD)0
Skewness3.2050704
Sum245474.1
Variance956.08245
MonotonicityNot monotonic
2025-08-12T23:14:39.418490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.36852494 2640
66.0%
80.1150267 1
 
< 0.1%
23.08850704 1
 
< 0.1%
79.7265667 1
 
< 0.1%
31.8308804 1
 
< 0.1%
18.93408812 1
 
< 0.1%
79.28126588 1
 
< 0.1%
29.86133951 1
 
< 0.1%
187.206135 1
 
< 0.1%
127.1526711 1
 
< 0.1%
Other values (1351) 1351
33.8%
ValueCountFrequency (%)
1.538953462 1
< 0.1%
1.979432214 1
< 0.1%
2.783880477 1
< 0.1%
3.055430904 1
< 0.1%
3.65247253 1
< 0.1%
3.676761668 1
< 0.1%
3.741668213 1
< 0.1%
3.8047543 1
< 0.1%
3.820618131 1
< 0.1%
3.917339963 1
< 0.1%
ValueCountFrequency (%)
426.8758594 1
< 0.1%
328.9581489 1
< 0.1%
310.9744543 1
< 0.1%
310.6601723 1
< 0.1%
308.0370267 1
< 0.1%
299.996568 1
< 0.1%
293.566818 1
< 0.1%
283.4170697 1
< 0.1%
272.2694432 1
< 0.1%
271.9188358 1
< 0.1%

maintenance_history
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0595588
Minimum0
Maximum8
Zeros185
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:39.496577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.0595588
median2.0595588
Q32.0595588
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83379718
Coefficient of variation (CV)0.40484262
Kurtosis7.4891344
Mean2.0595588
Median Absolute Deviation (MAD)0
Skewness1.1489104
Sum8238.2353
Variance0.69521774
MonotonicityNot monotonic
2025-08-12T23:14:39.517257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2.059558824 2640
66.0%
2 376
 
9.4%
1 328
 
8.2%
3 272
 
6.8%
0 185
 
4.6%
4 128
 
3.2%
5 45
 
1.1%
6 16
 
0.4%
7 8
 
0.2%
8 2
 
0.1%
ValueCountFrequency (%)
0 185
 
4.6%
1 328
 
8.2%
2 376
 
9.4%
2.059558824 2640
66.0%
3 272
 
6.8%
4 128
 
3.2%
5 45
 
1.1%
6 16
 
0.4%
7 8
 
0.2%
8 2
 
0.1%
ValueCountFrequency (%)
8 2
 
0.1%
7 8
 
0.2%
6 16
 
0.4%
5 45
 
1.1%
4 128
 
3.2%
3 272
 
6.8%
2.059558824 2640
66.0%
2 376
 
9.4%
1 328
 
8.2%
0 185
 
4.6%

load
Real number (ℝ)

High correlation 

Distinct1361
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.4966
Minimum65.433781
Maximum204.48381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-08-12T23:14:39.543118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum65.433781
5-th percentile91.6499
Q1103.4966
median103.4966
Q3103.4966
95-th percentile113.8378
Maximum204.48381
Range139.05003
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.1412376
Coefficient of variation (CV)0.078661888
Kurtosis45.550247
Mean103.4966
Median Absolute Deviation (MAD)0
Skewness4.3160976
Sum413986.38
Variance66.27975
MonotonicityNot monotonic
2025-08-12T23:14:39.572835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.4965957 2640
66.0%
90.93173156 1
 
< 0.1%
101.6441255 1
 
< 0.1%
107.5979949 1
 
< 0.1%
104.56067 1
 
< 0.1%
96.91659949 1
 
< 0.1%
105.0321837 1
 
< 0.1%
123.2555095 1
 
< 0.1%
101.3868547 1
 
< 0.1%
97.0927388 1
 
< 0.1%
Other values (1351) 1351
33.8%
ValueCountFrequency (%)
65.43378094 1
< 0.1%
71.82188524 1
< 0.1%
74.10543107 1
< 0.1%
74.53277196 1
< 0.1%
75.12886883 1
< 0.1%
76.63318444 1
< 0.1%
77.15998879 1
< 0.1%
77.66989628 1
< 0.1%
77.87919703 1
< 0.1%
78.01408925 1
< 0.1%
ValueCountFrequency (%)
204.4838128 1
< 0.1%
202.3605633 1
< 0.1%
200.2285105 1
< 0.1%
196.3679874 1
< 0.1%
192.8891422 1
< 0.1%
191.797222 1
< 0.1%
187.41856 1
< 0.1%
184.4450808 1
< 0.1%
182.9412094 1
< 0.1%
178.0788472 1
< 0.1%

failure
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
2230 
1.0
1770 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2230
55.8%
1.0 1770
44.2%

Length

2025-08-12T23:14:39.598382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:14:39.616618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2230
55.8%
1.0 1770
44.2%

Most occurring characters

ValueCountFrequency (%)
0 6230
51.9%
. 4000
33.3%
1 1770
 
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6230
51.9%
. 4000
33.3%
1 1770
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6230
51.9%
. 4000
33.3%
1 1770
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6230
51.9%
. 4000
33.3%
1 1770
 
14.8%

anomaly
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
3922 
1.0
 
78

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3922
98.0%
1.0 78
 
1.9%

Length

2025-08-12T23:14:39.633812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:14:39.647094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3922
98.0%
1.0 78
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 7922
66.0%
. 4000
33.3%
1 78
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7922
66.0%
. 4000
33.3%
1 78
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7922
66.0%
. 4000
33.3%
1 78
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7922
66.0%
. 4000
33.3%
1 78
 
0.7%

process_type_Oil Analysis
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
2600 
1.0
1400 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2600
65.0%
1.0 1400
35.0%

Length

2025-08-12T23:14:39.663621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:14:39.677196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2600
65.0%
1.0 1400
35.0%

Most occurring characters

ValueCountFrequency (%)
0 6600
55.0%
. 4000
33.3%
1 1400
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6600
55.0%
. 4000
33.3%
1 1400
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6600
55.0%
. 4000
33.3%
1 1400
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6600
55.0%
. 4000
33.3%
1 1400
 
11.7%

process_type_Vibrations
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
2760 
1.0
1240 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2760
69.0%
1.0 1240
31.0%

Length

2025-08-12T23:14:39.693909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:14:39.707592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2760
69.0%
1.0 1240
31.0%

Most occurring characters

ValueCountFrequency (%)
0 6760
56.3%
. 4000
33.3%
1 1240
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6760
56.3%
. 4000
33.3%
1 1240
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6760
56.3%
. 4000
33.3%
1 1240
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6760
56.3%
. 4000
33.3%
1 1240
 
10.3%

Interactions

2025-08-12T23:14:38.339331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.505248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.835690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.113619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.378042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.632694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.938854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.210137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.486407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.808215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.061709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.363806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.538731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.860547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.140034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.402121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.655096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.962820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.235356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.509566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.830244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.086250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.389456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.565465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.886501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.163866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.425623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.678807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.989034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.260949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.535375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.854046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.112834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.413723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.589317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.910706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.188515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.448338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.754919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.012613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.285737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.558606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.876869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.137627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.437236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.612822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.935937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.210702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.468980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.776387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.036982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.309353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.582622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.898794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.162398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.461048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.635999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.959447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.233581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.491012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.797661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.060174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.333571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.605715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.920921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.186126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.485876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.661550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.986460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.257424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.514339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.820912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.085413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.359225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.630924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.944258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.212053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.511920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.686239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.012408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.282420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.538531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.846000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.110371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.385299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.656241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.968834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.237966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.536240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.760138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.037956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.305447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.563521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.868637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.135237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.409729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.735110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.991425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.263253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.560425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.782344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.061383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.327730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.585079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.890831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.158697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.434122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.757717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.013699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.287258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.587147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:35.809851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.088169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.353870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.609648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:36.914844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.185289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.460331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:37.782919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.037843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:14:38.313312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-12T23:14:39.725257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
acidityanomalycontaminant_levelequipment_idfailurehours_operatedloadmaintenance_historyoil_qualitypressureprocess_type_Oil Analysisprocess_type_Vibrationstemperaturetime_stepvibration
acidity1.0000.0310.8930.0400.562-0.007-0.003-0.0090.949-0.0070.9320.4570.0010.019-0.002
anomaly0.0311.0000.0000.0200.0000.0000.5270.0000.4920.0240.0000.0060.0110.0270.577
contaminant_level0.8930.0001.0000.0400.504-0.000-0.000-0.0000.883-0.0000.8970.4390.0000.016-0.000
equipment_id0.0400.0200.0401.0000.115-0.038-0.041-0.0820.0310.0230.2650.3090.0020.0000.015
failure0.5620.0000.5040.1151.0000.4400.2280.2910.5150.0740.2740.0710.3200.1420.171
hours_operated-0.0070.000-0.000-0.0380.4401.0000.0160.0720.001-0.0290.4190.3830.0030.065-0.008
load-0.0030.527-0.000-0.0410.2280.0161.0000.0160.000-0.0110.3340.3040.0010.076-0.003
maintenance_history-0.0090.000-0.000-0.0820.2910.0720.0161.0000.002-0.0350.4170.3800.0040.001-0.010
oil_quality0.9490.4920.8830.0310.5150.0010.0000.0021.0000.0010.8730.428-0.0000.0240.000
pressure-0.0070.024-0.0000.0230.074-0.029-0.011-0.0350.0011.0000.3830.7830.192-0.0020.288
process_type_Oil Analysis0.9320.0000.8970.2650.2740.4190.3340.4170.8730.3831.0000.4910.4170.0000.315
process_type_Vibrations0.4570.0060.4390.3090.0710.3830.3040.3800.4280.7830.4911.0000.8520.0000.646
temperature0.0010.0110.0000.0020.3200.0030.0010.004-0.0000.1920.4170.8521.000-0.0990.829
time_step0.0190.0270.0160.0000.1420.0650.0760.0010.024-0.0020.0000.000-0.0991.000-0.097
vibration-0.0020.577-0.0000.0150.171-0.008-0.003-0.0100.0000.2880.3150.6460.829-0.0971.000

Missing values

2025-08-12T23:14:38.684899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-12T23:14:38.720115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

equipment_idtime_stepvibrationtemperaturepressureoil_qualitycontaminant_levelacidityhours_operatedmaintenance_historyloadfailureanomalyprocess_type_Oil Analysisprocess_type_Vibrations
01.01.00.33852520.31911532.28276153.89449176.129293138.17143580.1150272.090.9317320.00.00.00.0
11.02.00.33852520.31911532.28276153.89449176.129293138.17143521.3014091.0100.9983180.00.00.00.0
21.03.00.33852520.31911532.28276153.89449176.129293138.1714351.5389533.0100.5222180.00.00.00.0
31.04.00.33852520.31911532.28276153.89449176.129293138.1714354.3894812.085.6367030.00.00.00.0
41.05.00.33852520.31911532.28276153.89449176.129293138.17143533.9394553.0101.4512580.00.00.00.0
51.06.00.33852520.31911532.28276153.89449176.129293138.1714353.8047541.0110.0884290.00.00.00.0
61.07.00.33852520.31911532.28276153.89449176.129293138.17143513.0195811.096.2325850.00.00.00.0
71.08.00.33852520.31911532.28276153.89449176.129293138.17143545.9318120.099.0371430.00.00.00.0
81.09.00.33852520.31911532.28276153.89449176.129293138.171435135.1316833.0103.5348601.00.00.00.0
91.010.00.33852520.31911532.28276153.89449176.129293138.171435172.9864222.082.2432321.00.00.00.0
equipment_idtime_stepvibrationtemperaturepressureoil_qualitycontaminant_levelacidityhours_operatedmaintenance_historyloadfailureanomalyprocess_type_Oil Analysisprocess_type_Vibrations
3990100.031.00.33852520.31911532.28276153.89449176.129293138.17143538.7381734.096.9289410.00.00.00.0
3991100.032.00.33852520.31911532.28276153.89449176.129293138.17143520.7822971.0101.7084600.00.00.00.0
3992100.033.00.33852520.31911532.28276153.89449176.129293138.17143573.8296182.099.1986370.00.00.00.0
3993100.034.00.33852520.31911532.28276153.89449176.129293138.17143518.1938762.0106.5314370.00.00.00.0
3994100.035.00.33852520.31911532.28276153.89449176.129293138.17143538.8745953.096.3509420.00.00.00.0
3995100.036.00.33852520.31911532.28276153.89449176.129293138.17143596.9819883.0105.0552251.00.00.00.0
3996100.037.00.33852520.31911532.28276153.89449176.129293138.17143547.4311843.0104.2847710.00.00.00.0
3997100.038.00.33852520.31911532.28276153.89449176.129293138.17143520.0764980.0105.6028480.00.00.00.0
3998100.039.00.33852520.31911532.28276153.89449176.129293138.17143522.3068442.0109.4996420.00.00.00.0
3999100.040.00.33852520.31911532.28276153.89449176.129293138.171435176.8473534.096.9064111.00.00.00.0